An asynchronous hybrid pixel image sensor

Akrarai, Mohamed; Margotat, Nils; Sicard, Gilles; Fesquet, Laurent

doi:10.1109/async48570.2021.00016

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…For further details on DVS pixels, we refer the interested reader to [ 3 ], and for TFS pixels, readers may refer to [ 7 ].…”

Section: Event-based Image Sensorsmentioning

confidence: 99%

“…One solution is to combine a conventional camera with Active-Pixel Sensor (APS) pixels with a DVS to form a Dynamic and Active Pixel Vision Sensor (DAVIS) [ 14 ], but the resulting sensor is not fully event-based. Another fully event-based solution is to combine a DVS with Time to First Spike (TFS) pixels, as proposed in the Hybrid DVS + TFS Pixel [ 7 ] and the Asynchronous Time-based Image Sensor (ATIS) [ 15 ]. These sensors work as follows: whenever the DVS circuit outputs an event (i.e., a significant change has been detected), the TFS circuit starts its integration to measure the current light intensity.…”

Section: Event-based Image Sensorsmentioning

confidence: 99%

“…where ŶMS,n is the cluster's luminance, estimated at the arrival of the event n using the mean-shift approach; e lum is the luminance of the event n; p ∈ [0, 1] is a correction factor to compensate for the part of the BG; Ŷobj,n is the estimated luminance value of the object; and Y BG is the luminance of the background. The first Equation (10) calculates the influence of the new event on the estimated luminance, and the second Equation ( 11) corrects the influence of the background using the parameter p of (7). Ideally, p should be close to 1, but in most applications it is lower, depending on the contour of objects and uniformity of colors.…”

Section: Direct Estimationmentioning

confidence: 99%

“…However, these methods often require the accumulation of many events to replace the missing intensity measurements of conventional sensors. An alternative approach to enhance either the algorithm’s performance or the understanding of the scene is to incorporate intensity measurements from Time to First Spike (TFS) pixels into the DVS data stream [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Velocity and Color Estimation Using Event-Based Clustering

Lesage,

Tran,

Mancini

et al. 2023

Sensors

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Event-based clustering provides a low-power embedded solution for low-level feature extraction in a scene. The algorithm utilizes the non-uniform sampling capability of event-based image sensors to measure local intensity variations within a scene. Consequently, the clustering algorithm forms similar event groups while simultaneously estimating their attributes. This work proposes taking advantage of additional event information in order to provide new attributes for further processing. We elaborate on the estimation of the object velocity using the mean motion of the cluster. Next, we are examining a novel form of events, which includes intensity measurement of the color at the concerned pixel. These events may be processed to estimate the rough color of a cluster, or the color distribution in a cluster. Lastly, this paper presents some applications that utilize these features. The resulting algorithms are applied and exercised thanks to a custom event-based simulator, which generates videos of outdoor scenes. The velocity estimation methods provide satisfactory results with a trade-off between accuracy and convergence speed. Regarding color estimation, the luminance estimation is challenging in the test cases, while the chrominance is precisely estimated. The estimated quantities are adequate for accurately classifying objects into predefined categories.

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“…For further details on DVS pixels, we refer the interested reader to [ 3 ], and for TFS pixels, readers may refer to [ 7 ].…”

Section: Event-based Image Sensorsmentioning

confidence: 99%